Cycle consistency assumes that there exist some underlying relationships between the source and target domains, and tries to make supervision at the domain level. To be precise, we want to capture some special characteristics of one image collection and figure out how to translate these characteristics into the other image collection. To achieve this, Zhu et al. [1]} proposed the cycle consistency mechanism, where not only the mapping from the source domain to the target domain is learned, but also the backward mapping is combined. Specifically, given a source domain \(X\)  and a target domain \(Y\) , we have a translator \(G: X \rightarrow Y\)  and another translator \(F: Y \rightarrow X\)  that trained simultaneously to guarantee both an \(adversarial\ loss\)  that encourages \(G(X)\approx Y\)  and \(F(Y)\approx X\)  and a \(cycle\ consistency\ loss\)  that encourages \(F(G(X))\approx X\)  and \(G(F(Y))\approx Y\) .
